(1) Field of the Invention
The present invention relates generally to the field of electronic neural networks, and more particularly to a new architecture for neural networks having a plurality of hidden layers, or multi-layer neural networks, and further to new methodologies for providing supervised and unsupervised training of neural networks constructed according to the new architecture.
(2) Description of the Prior Art
Electronic neural networks have been developed to rapidly identify patterns in certain types of input data, or to accurately classify the input patterns into one of a plurality of predetermined classifications. For example, neural networks have been developed which can recognize and identify patterns, such as the identification of hand-written alphanumeric characters, in response to input data constituting the pattern of on/off picture elements, or xe2x80x9cpixels,xe2x80x9d representing the images of the characters to be identified. In such a neural network, the pixel pattern is represented by, for example, electrical signals coupled to a plurality of input terminals, which, in turn, are connected to a number of processing nodes, or neurons, each of which is associated with one of the alphanumeric characters which the neural network can identify. The input signals from the input terminals are coupled to the processing nodes through certain weighting functions, and each processing node generates an output signal which represents a value that is a non-linear function of the pattern of weighted input signals applied thereto. Based on the values of the weighted pattern of input signals from the input terminals, if the input signals represent a character which can be identified by the neural network, one of the processing nodes which is associated with that character will generate a positive output signal, and the others will not. On the other hand, if the input signals do not represent a character which can be identified by the neural network, none of the processing nodes will generate a positive output signal. Neural networks have been developed which can perform similar pattern recognition in a number of diverse areas.
The particular patterns which the neural network can identify depend on the weighting functions and the particular connections of the input terminals to the processing nodes, or elements. As an example, the weighting functions in the above-described character recognition neural network essentially will represent the pixel patterns which define each particular character. Typically, each processing node will perform a summation operation in connection with the weight values, also referred to as connection values or weighting values, representing the weighted input signals provided thereto, to generate a sum that represents the likelihood that the character to be identified is the character associated with that processing node. The processing node then applies the non-linear function to that sum to generate a positive output signal if the sum is, for example, above a predetermined threshold value. The non-linear functions which the processing nodes may use in connection with the sum of weighted input signals are generally conventional functions, such as step functions, threshold functions, or sigmoids. In all cases the output signal from the processing node will approach the same positive output signal asymptotically.
Before a neural network can be useful, the weighting functions for each of the respective input signals must be established. In some cases, the weighting functions can be established a priori. Normally, however, a neural network goes through a training phase, in which input signals representing a number of training patterns for the types of items to be classified (e.g., the pixel patterns of the various hand-written characters in the character-recognition example) are applied to the input terminals, and the output signals from the processing nodes are tested. Based on the pattern of output signals from the processing nodes for each training example, the weighting functions are adjusted over a number of trials. Once trained, a neural network can generally accurately recognize patterns during an operational phase. The degree of success is based in part on the number of training patterns applied to the neural network during the training stage and the degree of dissimilarity between patterns to be identified. Such a neural network can also typically identify patterns which are similar, but not necessarily identical, to the training patterns.
One of the problems with conventional neural network architectures as described above is that the training methodology, generally known as the xe2x80x9cback-propagationxe2x80x9d method, is often extremely slow in a number of important applications. Also, under the back-propagation method, the neural network may provide erroneous results which may require restarting the training. In addition, even after a neural network has been through a training phase, confidence that the best training has been accomplished may sometimes be poor. If a new classification is to be added to a trained neural network, the complete neural network must be retrained. Further, the weighting functions generated during the training phase often cannot be interpreted in ways that readily provide understanding of what they particularly represent.
Accordingly, it is an object of the present invention to provide a new and improved neural network architecture for use in pattern recognition in which the weighting functions are determined a priori.
Other objects and advantages of the present invention will become more obvious hereinafter in the specification and drawings.
In accordance with the present invention, a new neural network architecture, referred to hereinafter as a neural sensor, is part of a new neural network technology that is constructed rather then trained. Since the words xe2x80x9cneural networksxe2x80x9d often connote a totally trainable neural network, a constructed neural network is a connectionist neural network device that is assembled using common neural network components to perform a specific process. The constructed neural network assembly is analogous to the construction of an electronic assembly using resistors, transistors, integrated circuits and other simple electronic parts. A constructed neural network is fabricated using common neural network components such as processing elements (neurons), output functions, gain elements, neural network connections of certain types or of specific values and other artificial neural network parts. As in electronics, the design goal and the laws of nature such as mathematics, physics, chemistry, mechanics, and xe2x80x9crules of thumbxe2x80x9d are used to govern the assembly and architecture of a constructed neural network. A constructed neural network, which is assembled for a specific process without the use of training, can be considered equivalent to a trained neural network having accomplished an output error of zero after an infinite training sequence. Although there are some existing connective circuits that meet the design criteria of a constructed neural network, the term xe2x80x9cconstructed neural networkxe2x80x9d is used herein to differentiate this new neural technology which does not require training from the common neural network technology requiring training.
Constructed neural networks can be embodied in analog or digital technologies, or in software. Today one can find a blurring between the boundaries of analog and digital technologies. Some of the classic analog processing is now found in the realm of digital signal processing and classic digital processing is found in analog charged couple devices and sample and hold circuits especially in the area of discrete time signals and shift registers.
One of the components utilized in constructing a neural sensor is a neural director. In brief, a neural director receives an input vector X comprising xe2x80x9cIxe2x80x9d input components Xi and generates in response thereto, an output vector Y comprising xe2x80x9cJxe2x80x9d output components Yj, where xe2x80x9cIxe2x80x9d and xe2x80x9cJxe2x80x9d are the neural director""s input and output dimensions. The neural director has an input processing node layer comprised of xe2x80x9cIxe2x80x9d processing nodes and an output processing node layer comprised of xe2x80x9cJxe2x80x9d processing nodes. Each output processing node receives the outputs from the input processing nodes to which a weighting value w(i,j) has been applied and generates one of said output components Yj representing a linear function in connection therewith. The weighting values w(i,j) contain a unique internal representation of a uniform spatial distribution.
A neural director can be constructed to be one of two types, designated type 1 or type 2. The two types differ in what may be termed xe2x80x9cspatial linearityxe2x80x9d. In addition to classic linearity, i.e., the use of non-linear weighting functions in the neural circuit, spatial linearity includes a xe2x80x9clinearity in spacexe2x80x9d. In a fully populated single layer neural network which has xe2x80x9cIxe2x80x9d input processing nodes and xe2x80x9cJxe2x80x9d output processing nodes, each of the output processing nodes will contain xe2x80x9cIxe2x80x9d weight values. The xe2x80x9cIxe2x80x9d weight values of each processing node can be considered a vector of xe2x80x9cIxe2x80x9d components in an xe2x80x9cIxe2x80x9d dimensional space. One of the many important characteristics of a constructed neural network is that a classification of an input pattern is greatly defined by a vector""s direction in a multidimensional space. Thus, spatial linearity/nonlinearity affects the internal process of a neural sensor. An angular relationship between input and output vector pairs can be used to define the spatial linearity. A network is linear in space when the angles between all different vector pairs are the same in the output space as they are in the input space regardless of the dimensionalities of the spaces. A network is nonlinear if it is either classically and/or spatially nonlinear. A spatial nonlinearity causes an input vector pair to diverge in direction in the output space and is analogous to a system nonlinearity in chaos theory where two similar initial condition points diverge over time. A neural director type 1 is linear in both its neural circuit, i.e., classically linear, and in its space, i.e., spatially linear. Generally, a neural director type 2 is classically linear but spatially nonlinear, though it will be understood that either classic or spatial nonlinearity will result in a neural director type 2. When compared to a neural director type 1 of the same input and output dimensions, a neural director type 2 nonlinearly shifts an input vector away from the output direction which one would anticipate using the neural director type 1. A neural director type 2 produces a nonlinear gradient between two poles in its multidimensional output space, one pole lying in the center of a sub space that is directed by all positive elements and the other pole being the opposite polarity.
Spatial nonlinearity is a parameter for a constructed neural network connectionist device which affects the recognition differentiation between similar input patterns. Reduced to its most basic concept, a constructed neural network senses features from a specific input pattern to provide a deterministic direction through a connecting circuit as a feature vector. This deterministic direction in a multidimensional space is the information that is used for the recognition and classification of the pattern. The spatial nonlinearities of the type 2 neural director provide a process that allows the discrimination of finer details in the recognition of an input pattern. Spatial nonlinearity is the result of a deterministic change in a vector""s direction in its multidimensional space relative to its intended direction in a linear space. The dimensionalities between these spaces may be different or the same. While most conventional neural networks demonstrate a spatial nonlinearity, their spatial nonlinearity is primarily caused by the use of nonlinear neural neurons.
The neural director type 1 has several advantages in performing different operations depending upon its application in a network. A neural director type 1 has the ability to linearly transform a vector from one set of dimensions to the same or that of another set of dimensions. The type 1 neural director can fuse separate data paths into a single vector as each output element of the vector contains a composition of all input elements of the input data. The type 1 neural director may also distribute input data into different layers of like data and can expand its input data into higher dimensions, where the input data can be sensed at a higher resolution than it can in its lower dimension. Although the dimensions are not totally independent, the dimensional independency can be increased when the type 1 neural director is coupled with a spatially nonlinear device. The neural director type 1 can represent a generalized matched filter which contains all possible combinations of input patterns due to its distributed connection set. The type 1 neural director can linearly expand input data or can use nonlinear output functions, which when applied to a conventional neural network in lieu of the original data will make the conventional network learn faster. Depending on the resolution chosen for the internal representation of the uniform spatial distribution, a neural director type 1 may be called a xe2x80x9cnearxe2x80x9d ideal neural director type 1. A near ideal neural director type 1 remains linear in its neural circuit but it is slightly nonlinear in space because the position of a vector in the neural director""s output space will be altered relative to the vector""s ideal position in a linear space. Used in a multilayer neural director, the near ideal neural director type 1, without other nonlinearities, increases the recognition resolution of similar input patterns.
The neural sensor utilizes combinations of neural directors and other neural network components to form a pattern array former, first and second order feature processors, a feature fusion processor and a pattern classification network. The pattern array former organizes the raw input data into the proper array format for use in the first and second order feature processors. The first order feature processor receives the formatted data from the array former and generates in response a first order feature vector illustrative of first order features of the input data. The first order feature processor includes a neural director which generates the first order feature vector to have a greater dimensionality than the input data. The greater dimensionality aids in discriminating between similar patterns in the input data. The second order feature processor also receives formatted data from the array former and generates in response at least one second order feature vector illustrative of gradients in the input data, where each gradient contains multiple local frequency or spatial frequency data depending on the form of the raw input data. The second order feature processor includes at least one neural director that generates the second order feature vector to have a greater dimensionality than the gradient, aiding in discriminating between similar patterns in the gradient. The feature fusion processor receives the first and second order feature vectors from the first and second order feature processors and generates a single fused feature vector. The pattern classification network receives the fused feature vector and generates in response a pattern classification.